Artificial Intelligence
Autonomous agents that carry out multi-step tasks on their own, not just answer a single prompt.
01The problem
Getting a model to produce an impressive answer once is easy. Getting it to behave reliably on thousands of messy, real inputs — and to fail safely when it doesn’t — is where most AI projects quietly stall.
The gap is rarely the model itself. It’s the missing pieces around it: clean inputs, clear boundaries on what the AI is allowed to do, and a way to tell whether it’s actually working.
02Our approach
We begin by mapping the exact task the AI should take on and what a human or a simple rule would have done instead. If a rule does the job, we’ll recommend the rule — AI has to earn its complexity.
When it does earn it, we prototype against your real data early, so quality is judged on your inputs rather than a polished demo.
03Under the hood
Production AI needs more than a prompt. We build retrieval over your own content where it helps, structured outputs the rest of your system can trust, and fallbacks for when the model is unsure or wrong.
Every change is checked against an evaluation set of real examples, so improvements are measured instead of guessed — and regressions are caught before users see them.
04What you walk away with
You get the code, the prompts, the evaluation set and the logging — everything needed to understand what the AI did and why.
The integration is built so the underlying model can be swapped as better or cheaper ones appear, without rebuilding the product around it.
AI features built into the product your users already use — not a chatbot bolted onto the corner of the screen.
Assistants, summarisation and generation built into an existing product.
Agents that carry out multi-step tasks using your tools and data.
Pipelines that move work between systems without manual copy-paste.
Pull structured data out of PDFs, emails and forms reliably.
Chat assistants that answer from your own knowledge base, with hand-off to humans.
Forecasting and scoring models trained on your historical data.
Scope
Identify where AI is genuinely better than a simple rule — and where it isn’t worth the complexity.
Prototype
A small working version against real inputs, so quality is judged on your data, not a demo.
Build
Production integration with evaluation, guardrails, and fallbacks for when the model is wrong.
Launch
Rolled out with logging and monitoring so behaviour stays visible after go-live.
Improve
Tune prompts, retrieval and flows based on what real usage shows.
We don't have a published AI Agent Development case study yet — here's a selection of other live products we've built.
The real tools behind AI Agent Development — not the full company stack, just what applies here.
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The services we build on and integrate with most often. Hover a tile to see its name.
Senior
Senior designers, engineers and AI specialists — not a roster padded with junior hires and account managers. Every project is led by the people who scoped it.
AI-first
We use modern AI tooling throughout the build itself — from architecture to code — which is how a small team ships at a pace larger agencies can’t match. AI isn’t a feature we bolt onto client products for its own sake.
End-to-end
Strategy, design, engineering, and launch stay with the same team instead of being handed between vendors. You talk to the people building your product, not an intermediary relaying messages.
Yours to own
Full repository access and IP transfer at launch. We work for you, not as a dependency you have to keep paying to maintain access to your own product.
Not always — and we’ll say so. If a well-designed rule or a simpler workflow does the job, that’s what we’ll recommend. AI earns its place where inputs are too varied or unstructured for rules to cover.
Let's Talk
Tell us what you're building — we'll scope it honestly, including if it's not a fit.
